A database is an electronic filing system that stores data in a structured way. The primary storage structure in a database is a table. A database may contain multiple tables and each table may hold information of a specific type. Database tables store and organize data in horizontal rows and vertical columns. Rows typically correspond to real-world entities or relationships that represent individual records in a table. Columns may denote specific attributes of those entities or relationships, such as “name,” “address” or “phone number.” For example, Company X may have a database containing a “customer” table listing the names, addresses and phone numbers of its customers. Each row may represent a single customer and the columns may represent each customer's name, address and phone number.
Databases are generally stored in computer memory that is one-dimensional. Two-dimensional database tables must therefore be mapped onto a one-dimensional data structure to be stored within a database. One mapping approach involves storing a table in a database row-by-row (i.e., a row-oriented storage model). This approach keeps information about a single entity together. For example, row-by-row storage may store all information about a first customer first, then all information about a second customer and so on. Alternatively, a table may be stored in a database column-by-column (i.e., a column-oriented storage model). This approach keeps like attributes of different entities together. For example, column-by-column storage may store all customer names first, then all customer addresses and so on.
Data must generally be accessed from a table in the same manner that it was stored. That is, conventional computer storage techniques require dedicated query operators that can access specific types of storage models. For example, row query operators are used to process data stored in a database in row-formatted storage models and column query operators are used to process data stored in column-formatted storage models. Choosing which storage model to use thus often depends on how data will be used. Row-oriented storage models are commonly well-suited for transactional queries, while column-oriented storage models are generally well-suited for analytical queries. Accordingly, conventional query processing schemes are tightly bound to the underlying storage model of the database being queried.
In reality, however, a database having certain data stored in a column-formatted storage model may be asked to handle a transactional query relating to that data or a database having certain data stored in a row-formatted storage model may be asked to handle an analytical query relating to that data. For example, a database having data stored in a row-formatted storage model may receive a mixed set of queries requiring transactional and analytical processing of that data. Conventional mechanisms for handling a mixed set of queries involve altering the structure or format of the database storage model of the queried data to a hybrid configuration of row- and column-formatted storage models. For example, Partition Attributes Across (PAX) groups together all values of each column within each page and Oracle Exadata groups column values within each compression unit. A single hybrid storage model advantageously provides row- and column-formatted storage models and may give better transactional performance than pure column-formatted storage and better analytical performance than pure row-formatted storage.